LSTM-Based Path Prediction for Effective Sensor Filtering in Sensor Registry System

Abstract

The Internet of Things (IoT) is expected to provide intelligent services by receiving heterogeneous data from ambient sensors. A mobile device employs a sensor registry system (SRS) to present metadata from ambient sensors, then connects directly for meaningful data. The SRS should provide metadata for sensors that may be successfully connected. This process is location-based and is also known as sensor filtering. In reality, GPS sometimes shows the wrong position and thus leads to a failed connection. We propose a dual collaboration strategy that simultaneously collects GPS readings and predictions from historical trajectories to improve the probability of successful requests between mobile devices and ambient sensors. We also update the evaluation approach of sensor filtering in SRS by introducing a Monte Carlo-based simulation flow to measure the service provision rate. The empirical study shows that the LSTM-based path prediction can compensate for the loss of location abnormalities and is an effective sensor filtering model.

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

“…After classification, the sensor hub data must be fused; so the model must be trained to compare the data extracted by the machine learning classification [ 26 ]. As the tests do not contain named data, an unsupervised data fusion calculation SAE-MDA1 based on SAEM1 and a supervised data fusion calculation SAE-MDA2 based on SAEM2 were designed.…”